Infective endocarditis (IE) is one of the most serious medical complications associated with intravenous drug use (IDU), with significant morbidity and mortality occurring in unrecognized cases. Presenting clinical features associated with IE have been difficult to define. Since fever occurs in nearly 98% of cases of IE in IDUs, current practice is to hospitalize all febrile IDUs for further evaluation. This represents a significant financial cost to hospitals, as nearly 90% of these patients are ultimately found not to have IE. The overall goal of this study is to develop and implement a safe and cost-effective Emergency Department (ED)- based algorithm for the evaluation of febrile IDUs at risk for IE. We hypothesize that, by taking advantage of recent developments which combine findings from advanced diagnostic testing (including echocardiography) and clinical assessment, this algorithm will enable ED physicians to more quickly and reliably stratify patients at risk for IE. We further hypothesize that disposition of the subset of patients in whom IE has been ruled-out, to less resource-intensive settings will reduce the number of hospitalizations without increasing adverse events, thus promoting more efficient use of clinical resources. In phase 1 (ongoing), patients who meet inclusion criteria are admitted to the General Clinical Research Center for a minimum of 48 hours for further evaluation and treatment. Findings from standard clinical, laboratory, and imaging tests, as well as a novel laboratory based diagnostic tool (PCR), will be utilized to design alternative diagnostic strategies, which will allow ED physicians to predict with increased specificity which febrile IDUs are likely to either have IE and/or develop complications that require hospitalization. Data will then be used to evaluate the relative costs of each diagnostic strategy, taking into account costs at both the individual and societal level. Calculation of potential' cost savings is in the range of several hundred million dollars. The findings and decision algorithms developed here will ultimately be employed to develop a larger study (phase 2), intended to validate the clinical efficacy of our new guidelines, and determine the true cost differential associated with a change in clinical practice.